SUSTAINABILITY ASSESSMENT OF LIVESTOCK PRODUCTION SYSTEMS IN THE PROVINCE OF OXAPAMPA / PASCO / PERU

• Autores: Tongo Pizarro , Eliseo, Soplín Villacorta, Hugo
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Nacional Agraria La Molina
• Repositorio: Revistas - Universidad Nacional Agraria La Molina
• Lenguaje: español
• OAI Identifier: oai:revistas.lamolina.edu.pe:article/1876
• Enlace del recurso: https://revistas.lamolina.edu.pe/index.php/eau/article/view/1876
• Nivel de acceso: acceso abierto
• Materia: ESMIS; agglomerative hierarchical cluster; distance proximity; criteria or methods of agglomeration or clustering; MESMIS; clúster jerárquico aglomerativo; distancia de proximidad; criterios o métodos de aglomeración o agrupamiento

The sustainability of livestock production systems in the districts of Oxapampa, Chontabamba and Huancabamba Oxapampa, Pasco Region, Peru, were studied. The methodology used was the MESMIS analysis. For the characterization of livestock systems, the producers were grouped into three groups: traditional extensive livestock system, semi-extensive livestock system and extensive livestock system. In the first instance, environmental sustainability, economic sustainability and social sustainability were partially evaluated with their respective indicators, and then evaluated simultaneously in an integrated manner. Through the MESMIS analysis, the traditional extensive livestock system was the most sustainable. Subsequently, the same producers with their environmental, economic and social indicators were subjected to the agglomerative hierarchical cluster analysis, using as proximity distance the Euclidean distance and with the agglomeration methods: nearest neighbor, nearest neighbor, average or intergroup linkage, bonding intragroup cluster centroid, Ward grouping medium and method with SPSS. The unique solution of three group clusters resulted in the similarity of a cluster with the same producers, obtained in the traditional livestock system using MESMIS with several of the agglomeration methods mentioned above, both at the partial and integral analysis level.